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Online dynamic ensemble deep random vector functional link neural network for forecasting

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Version 2 2023-10-22, 10:14
Version 1 2023-07-10, 12:35
journal contribution
revised on 2023-10-22, 10:12 and posted on 2023-10-22, 10:14 authored by Ruobin Gao, Ruilin Li, Minghui Hu, P.N. Suganthan, Kum Fai Yuen

This paper proposes a three-stage online deep learning model for time series based on the ensemble deep random vector functional link (edRVFL). The edRVFL stacks multiple randomized layers to enhance the single-layer RVFL’s representation ability. Each hidden layer’s representation is utilized for training an output layer, and the ensemble of all output layers forms the edRVFL’s output. However, the original edRVFL is not designed for online learning, and the randomized nature of the features is harmful to extracting meaningful temporal features. In order to address the limitations and extend the edRVFL to an online learning mode, this paper proposes a dynamic edRVFL consisting of three online components, the online decomposition, the online training, and the online dynamic ensemble. First, an online decomposition is utilized as a feature engineering block for the edRVFL. Then, an online learning algorithm is designed to learn the edRVFL. Finally, an online dynamic ensemble method, which can measure the change in the distribution, is proposed for aggregating all layers’ outputs. This paper evaluates and compares the proposed model with state-of-the-art methods on sixteen time series. 

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Published in: Neural Networks
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Open Access funding provided by the Qatar National Library



  • English



Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License

Institution affiliated with

  • Qatar University
  • College of Engineering - QU
  • KINDI Center for Computing Research - QU